\section{Conclusion}\label{sec:conc}
\vspace{-0.1in}
We presented fast and fully decentralized algorithms for performing several random walks in distributed dynamic networks. Our algorithms satisfy strong round complexity guarantees and is the first work to present robust techniques for this fundamental graph primitive in dynamic graphs.
 We further extend the work to show how it can be used for efficient sampling and other applications such as token dissemination. 
 %Our bounds for the token dissemination problem improve on previously best known algorithms under a suitably general dynamic graph update model. 
Our work opens several interesting research directions. 
 In the recent years, several fundamental graph operatives are being explored in various distributed dynamic models, and it would be interesting to explore further along these lines and obtain new approaches for identifying sparse cuts or graph partitioning, and similar spectral quantities. 
%As a specific question, it remains open whether the random walk techniques and subsequent bounds presented in this paper are optimal. 
 %Specifically are the bounds for estimating the dynamic mixing time, as well as the gossip dissemination problem tight? If yes, can one improve upon the bounds for somewhat more restrictive graph update models? 
 Finally, these algorithmic ideas may be useful building blocks in designing fully dynamic self-aware distributed graph systems. 
 It would be interesting to additionally consider total message complexity costs for these algorithms explicitly.%, even though they are implicitly encapsulated within the local per-edge bandwidth constraints of the CONGEST model. 